Multiple and least energy sign-changing solutions for Schrodinger-Poisson equations in R3 with restraint

In this paper, we study the existence of multiple sign-changing solutions with a prescribed Lp+1−norm and theexistence of least energy sign-changing restrained solutions for the following nonlinear Schr¨odinger-Poisson system:By choosing a proper functional restricted on some appropriate subset to using a method of invariant sets of descending flow,we prove that this system has infinitely many sign-changing solutions with the prescribed Lp+1−norm and has a least energy forsuch sign-changing restrained solution for p ∈ (3, 5). Few existence results of multiple sign-changing restrained solutions areavailable in the literature. Our work generalize some results in literature

Multi-Stage Generalized Deferred Acceptance Mechanism: Strategyproof Mechanism for Handling General Hereditary Constraints

The theory of two-sided matching has been extensively developed and applied to many real-life application domains. As the theory has been applied to increasingly diverse types of environments, researchers and practitioners have encountered various forms of distributional constraints. Arguably, the most general class of distributional constraints would be hereditary constraints; if a matching is feasible, then any matching that assigns weakly fewer students at each college is also feasible. However, under general hereditary constraints, it is shown that no strategyproof mechanism exists that simultaneously satisfies fairness and weak nonwastefulness, which is an efficiency (students' welfare) requirement weaker than nonwastefulness. We propose a new strategyproof mechanism that works for hereditary constraints called the Multi-Stage Generalized Deferred Acceptance mechanism (MS-GDA). It uses the Generalized Deferred Acceptance mechanism (GDA) as a subroutine, which works when distributional constraints belong to a well-behaved class called hereditary M$^\natural$-convex set. We show that GDA satisfies several desirable properties, most of which are also preserved in MS-GDA. We experimentally show that MS-GDA strikes a good balance between fairness and efficiency (students' welfare) compared to existing strategyproof mechanisms when distributional constraints are close to an M$^\natural$-convex set.Comment: 23 page

Energy Route Multi-Objective Optimization of Wireless Power Transfer Network: An Improved Cross-Entropy Method

This paper identifies the Wireless Power Transfer Network (WPTN) as an ideal model for long-distance Wireless Power Transfer (WPT) in a certain region with multiple electrical equipment. The schematic circuit and design of each power node and the process of power transmission between the two power nodes are elaborated. The Improved Cross-Entropy (ICE) method is proposed as an algorithm to solve for optimal energy route. Non-dominated sorting is introduced for optimization. A demonstration of the optimization result of a 30-nodes WPTN system based on the proposed algorithm proves ICE method to be efficacious and efficiency

Survival of Juvenile Silver Pomfret, Pampus argenteus, Kept in Transport Conmditions in Different Densities and Temperatures

Optimum conditions for the transportation of juvenile silver pomfret, Pampus argenteus (Euphrasen 1788) were investigated under simulated conditions. Juveniles (5.22±1.09 g) were kept in 10-l plastic bags containing oxygen and 3 l water at 15, 20, and 25°C in (a) low loading densities (5, 10, 20 g/l) for 8 h and (b) high loading densities (20, 30, 40 g/l) for 4 h. Following simulations, water was sampled to measure dissolved oxygen, pH, and total ammonia. Both survival rates and dissolved oxygen levels decreased when the temperature and loading density increased; pH decreased significantly under all transportation conditions. High loading density (30-40 g/l) and temperature (25°C) resulted in high total ammonia and mortality (54-64%). The ideal temperature for transporting juvenile silver pomfret in plastic bags was 15°C. At this temperature, the highest survival rate was recorded, even at loading densities of 40 g/l. Transport at high temperature (25°C) and loading density (20 g/l) should not exceed 8 h, due to raised mortality (>30%) and ammonia levels

A Comparison of Transit-Oriented Development in Sendai, Japan and Adelaide, Australia

Transit-oriented development (TOD) has the aim of encouraging sustainable mobility and urban development, especially where automobile use overshadows public transport. Therefore, looking to Japan with its history of development centred around railway stations and high public transport use can be useful. This paper examines TOD planning policies and approaches in Sendai, Japan, and Adelaide, Australia to find potential experience and strategies that might be applied to Adelaide. Results indicate that Sendai’s experience with business diversification, functional activities within TOD planning, and attracting private sector and landowner’s involvement in urban renewal projects, may bring fresh insights to TOD development in Adelaide

Spatial–temporal variations of NDVI and its response to climate in China from 2001 to 2020

Vegetation plays an important role in global or regional environmental change. In this study, the spatial–temporal variations of NDVI and its response to climate in China and its seven sub-regions were investigated based on MODIS NDVI data, ERA5-land precipitation (PRE) and temperature (TEM) data from 2001 to 2020. The inter-annual growth rate of NDVI in China was 0.0021/yr in the past 20 years. The inter-annual growth rates of NDVI in seven sub-regions had significant differences at regional or seasonal scales. The ratio of improved vegetation area to the total studied area reached about 70%. In summer, vegetation degradation was concentrated in East China and Southwest China. The vegetation in Central China and South China improved more obviously in autumn than in the other seasons. The vegetation of Northeast China had a remarkable degradation in autumn and winter, especially in winter. The influence degree of PRE (q = 0.54, P < 0.01) was greater than that of TEM (q = 0.27, P < 0.01) in the control of the spatial distribution of NDVI. The interaction influence degree q of PRE $\cap$ TEM was about 0.71 in the last 20 years. However, the PRE and TEM played different roles in vegetation growth in seven sub-regions

Scientometric Analysis and Mapping of Transit-Oriented Development Studies

Contemporary research on transit-oriented development (TOD) continues to progress within the context of sustainable development. Based on a scientometric analysis, this paper collected 507 articles from the Web of Science within the timespan of 1996–2021, and used VOSviewer to visually map and analyse the development of TOD studies, including yearly article distribution, main countries, organisations, highly co-cited documents, and burst keywords. We found documents with high co-citation strength in four clusters of TOD studies: impacts of TOD planning factors on transportation benefits; TOD typology, classification, and measurement; TOD contexts, experiences, difficulties, and solutions; TOD transit proximity and housing values

Substation equipment temperature prediction based on multivariate information fusion and deep learning network

Background Substation equipment temperature is difficult to achieve accurate prediction because of its typical seasonality, periodicity and instability, complex working environment and less available characteristic information. Methods To overcome these difficulties, a substation equipment temperature prediction method is proposed based on multivariate information fusion, convolutional neural network (CNN) and gated recurrent unite (GRU) in this article. Firstly, according to the correlation analysis including linear correlation mapping, autocorrelation function and partial autocorrelation function for substation equipment temperature data, the feature vectors from ambient, time and space are determined, that is the multivariate information fusion feature vector (denoted as MIFFV); secondly, the dimension of MIFFV is reduced by principal component analysis (PCA), extract some of the most important features and form the reduced feature vector (denoted as RFV); then, CNN is used for deep learning to extract the relationship between RFV and the high-dimensional space feature, and construct the high-dimensional feature vector of multivariate time series (denoted as HDFV); finally, the high-dimensional feature vector is used to train GRU deep learning network and predict the equipment temperature. Results A substation equipment in Taizhou City, Zhejiang Province is conducted by the method proposed in this article. Through the comparative experiment from the two aspects of features and methods, under the two prediction performance evaluation indexes of mean absolute percentage error (MAPE) and root mean square error (RSME), two main conclusions are drawn: (1) MIFFV from three aspects of ambient features, time features and space features have better prediction performance than the single feature vector and the combined feature vector of two aspects; (2) compared with other four related models under the same conditions, RFV is regarded as the input of the models, the proposed model has better prediction performance